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BMC veterinary research2021; 17(1); 78; doi: 10.1186/s12917-021-02781-5

Evaluation of fasting plasma insulin and proxy measurements to assess insulin sensitivity in horses.

Abstract: Proxies are mathematical calculations based on fasting glucose and/or insulin concentrations developed to allow prediction of insulin sensitivity (IS) and β-cell response. These proxies have not been evaluated in horses with insulin dysregulation. The first objective of this study was to evaluate how fasting insulin (FI) and proxies for IS (1/Insulin, reciprocal of the square root of insulin (RISQI) and the quantitative insulin sensitivity check index (QUICKI)) and β-cell response (the modified insulin-to-glucose ratio (MIRG) and the homeostatic model assessment of β-cell function (HOMA-β)) were correlated to measures of IS (M index) using the euglycemic hyperinsulinemic clamp (EHC) in horses with insulin resistance (IR) and normal IS. A second objective was to evaluate the repeatability of FI and proxies in horses based on sampling on consecutive days. The last objective was to investigate the most appropriate cut-off value for the proxies and FI. Results: Thirty-four horses were categorized as IR and 26 as IS based on the M index. The proxies and FI had coefficients of variation (CVs) ≤ 25.3 % and very good reliability (intraclass correlation coefficients ≥ 0.89). All proxies and FI were good predictors of the M index (r = 0.76-0.85; P < 0.001). The proxies for IS had a positive linear relationship with the M index whereas proxies for β-cell response and FI had an inverse relationship with the M index. Cut-off values to distinguish horses with IR from horses with normal IS based on the M index were established for all proxies and FI using receiver operating characteristic curves, with sensitivity between 79 % and 91 % and specificity between 85 % and 96 %. The cut-off values to predict IR were < 0.32 (RISQI),  9.5 µIU/mL for FI. Conclusions: All proxies and FI provided repeatable estimates of horses' IS. However, there is no advantage of using proxies instead of FI to estimate IR in the horse. Due to the heteroscedasticity of the data, proxies and FI in general are more suitable for epidemiological studies and larger clinical studies than as a diagnostic tool for measurement of IR in individual horses.
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Publication Date: 2021-02-15 PubMed ID: 33588833PubMed Central: PMC7885592DOI: 10.1186/s12917-021-02781-5Google Scholar: Lookup
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Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

The research evaluates different mathematical predictions of insulin sensitivity in horses and demonstrates their effectiveness and consistency, concluding that fasting insulin is an adequately predictive tool.

Objectives and Approach

  • The main goal of this research was to understand how different proxy measurements (mathematical estimations) for insulin sensitivity (IS) and β-cell response, such as the reciprocal of the square root of insulin (RISQI) and the quantitative insulin sensitivity check index (QUICKI), correspond to actual measures of IS in horses with insulin resistance (IR) and normal insulin sensitivity.
  • A secondary goal was to gauge the repeatability of these mathematical measures through day-to-day testing.
  • The research also aimed to establish optimal cut-off values for these proxies and fasting insulin (FI), enabling better discrimination between horses with IR and those with normal IS.

Findings

  • The study found that all tested proxies and FI demonstrated significantly high reliability (intraclass correlation coefficients ≥ 0.89) and low variation (coefficients of variation ≤ 25.3 %).
  • The proxies for IS had a direct relationship with the M index (a measure of insulin sensitivity), while proxies for β-cell response and FI showed an inverse relationship. This indicates they are suitable predictors of insulin resistance and sensitivity.
  • Researchers established cut-off values for all proxies and FI, distinguishing horses with IR from horses with normal IS, with sensitivities ranging between 79% and 91%, and specificities between 85% and 96%.

Implications

  • The research concluded that while these proxies reliably estimated a horse’s IS, using proxies doesn’t provide any significant advantage over tracking fasting insulin to estimate insulin resistance in horses.
  • The authors note that due to the heteroscedasticity (unequal variability) of the data, these proxies and FI tests are more suitable for epidemiological studies or larger clinical experiments as opposed to assessing insulin resistance on an individual horse level.

Cite This Article

APA
Lindåse S, Nostell K, Bergsten P, Forslund A, Bröjer J. (2021). Evaluation of fasting plasma insulin and proxy measurements to assess insulin sensitivity in horses. BMC Vet Res, 17(1), 78. https://doi.org/10.1186/s12917-021-02781-5

Publication

BMC veterinary research
ISSN: 1746-6148
NlmUniqueID: 101249759
Country: England
Language: English
Volume: 17
Issue: 1
Pages: 78
PII: 78

Researcher Affiliations

Lindåse, Sanna
  • Department of Clinical Sciences, Swedish University of Agricultural Sciences, Box 7054, 750 07, Uppsala, Sweden. sanna.lindase@slu.se.
Nostell, Katarina
  • Department of Clinical Sciences, Swedish University of Agricultural Sciences, Box 7054, 750 07, Uppsala, Sweden.
Bergsten, Peter
  • Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden.
  • Department of Medical Cell Biology, Uppsala University, Uppsala, Sweden.
Forslund, Anders
  • Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden.
Bröjer, Johan
  • Department of Clinical Sciences, Swedish University of Agricultural Sciences, Box 7054, 750 07, Uppsala, Sweden.

MeSH Terms

  • Animals
  • Female
  • Glucose Clamp Technique / veterinary
  • Horse Diseases / blood
  • Horse Diseases / metabolism
  • Horses
  • Insulin / blood
  • Insulin Resistance
  • Insulin-Secreting Cells / physiology
  • Male

Conflict of Interest Statement

None of the authors of this paper has a financial or personal relationship with other people or organizations that could inappropriately influence or bias the content of the article.

References

This article includes 32 references
  1. Frank N, Tadros EM. Insulin dysregulation.. Equine Vet J 2014 Jan;46(1):103-12.
    doi: 10.1111/evj.12169pubmed: 24033478google scholar: lookup
  2. Patterson-Kane JC, Karikoski NP, McGowan CM. Paradigm shifts in understanding equine laminitis.. Vet J 2018 Jan;231:33-40.
    doi: 10.1016/j.tvjl.2017.11.011pubmed: 29429485google scholar: lookup
  3. Bertin FR, de Laat MA. The diagnosis of equine insulin dysregulation.. Equine Vet J 2017 Sep;49(5):570-576.
    doi: 10.1111/evj.12703pubmed: 28543410google scholar: lookup
  4. Muniyappa R, Lee S, Chen H, Quon MJ. Current approaches for assessing insulin sensitivity and resistance in vivo: advantages, limitations, and appropriate usage.. Am J Physiol Endocrinol Metab 2008 Jan;294(1):E15-26.
    doi: 10.1152/ajpendo.00645.2007pubmed: 17957034google scholar: lookup
  5. Kahn SE, Prigeon RL, McCulloch DK, Boyko EJ, Bergman RN, Schwartz MW, Neifing JL, Ward WK, Beard JC, Palmer JP. Quantification of the relationship between insulin sensitivity and beta-cell function in human subjects. Evidence for a hyperbolic function.. Diabetes 1993 Nov;42(11):1663-72.
    doi: 10.2337/diab.42.11.1663pubmed: 8405710google scholar: lookup
  6. Katz A, Nambi SS, Mather K, Baron AD, Follmann DA, Sullivan G, Quon MJ. Quantitative insulin sensitivity check index: a simple, accurate method for assessing insulin sensitivity in humans.. J Clin Endocrinol Metab 2000 Jul;85(7):2402-10.
    doi: 10.1210/jcem.85.7.6661pubmed: 10902785google scholar: lookup
  7. Treiber KH, Kronfeld DS, Hess TM, Boston RC, Harris PA. Use of proxies and reference quintiles obtained from minimal model analysis for determination of insulin sensitivity and pancreatic beta-cell responsiveness in horses.. Am J Vet Res 2005 Dec;66(12):2114-21.
    doi: 10.2460/ajvr.2005.66.2114pubmed: 16379656google scholar: lookup
  8. Lindåse S, Nostell K, Söder J, Bröjer J. Relationship Between β-cell Response and Insulin Sensitivity in Horses based on the Oral Sugar Test and the Euglycemic Hyperinsulinemic Clamp.. J Vet Intern Med 2017 Sep;31(5):1541-1550.
    doi: 10.1111/jvim.14799pmc: PMC5598889pubmed: 28796307google scholar: lookup
  9. Bergman RN, Phillips LS, Cobelli C. Physiologic evaluation of factors controlling glucose tolerance in man: measurement of insulin sensitivity and beta-cell glucose sensitivity from the response to intravenous glucose.. J Clin Invest 1981 Dec;68(6):1456-67.
    doi: 10.1172/JCI110398pmc: PMC370948pubmed: 7033284google scholar: lookup
  10. Bamford NJ, Potter SJ, Harris PA, Bailey SR. Breed differences in insulin sensitivity and insulinemic responses to oral glucose in horses and ponies of moderate body condition score.. Domest Anim Endocrinol 2014 Apr;47:101-7.
    doi: 10.1016/j.domaniend.2013.11.001pubmed: 24308928google scholar: lookup
  11. Borer K, Bailey S, Menzeies-Gow N, Harris P, Elliott J. Use of proxy measurements of insulin sensitivity and insulin secretory response to distinguish between normal and previously laminitic ponies. Equine Vet J 2012;44(4):444–8.
    doi: 10.1111/j.2042-3306.2011.00475.xgoogle scholar: lookup
  12. Treiber KH, Kronfeld DS, Hess TM, Byrd BM, Splan RK, Staniar WB. Evaluation of genetic and metabolic predispositions and nutritional risk factors for pasture-associated laminitis in ponies.. J Am Vet Med Assoc 2006 May 15;228(10):1538-45.
    doi: 10.2460/javma.228.10.1538pubmed: 16677122google scholar: lookup
  13. Lindåse S, Müller C, Nostell K, Bröjer J. Evaluation of glucose and insulin response to haylage diets with different content of nonstructural carbohydrates in 2 breeds of horses.. Domest Anim Endocrinol 2018 Jul;64:49-58.
    doi: 10.1016/j.domaniend.2018.03.006pubmed: 29751248google scholar: lookup
  14. Carslake HB, Argo CM, Pinchbeck GL, Dugdale AHA, McGowan CM. Insulinaemic and glycaemic responses to three forages in ponies.. Vet J 2018 May;235:83-89.
    doi: 10.1016/j.tvjl.2018.03.008pubmed: 29704944google scholar: lookup
  15. Olley RB, Carslake HB, Ireland JL, McGowan CM. Comparison of fasted basal insulin with the combined glucose-insulin test in horses and ponies with suspected insulin dysregulation.. Vet J 2019 Oct;252:105351.
    doi: 10.1016/j.tvjl.2019.105351pubmed: 31554591google scholar: lookup
  16. Dunbar LK, Mielnicki KA, Dembek KA, Toribio RE, Burns TA. Evaluation of Four Diagnostic Tests for Insulin Dysregulation in Adult Light-Breed Horses.. J Vet Intern Med 2016 May;30(3):885-91.
    doi: 10.1111/jvim.13934pmc: PMC4913564pubmed: 27013065google scholar: lookup
  17. Kahn SE, Carr DB, Faulenbach MV, Utzschneider KM. An examination of beta-cell function measures and their potential use for estimating beta-cell mass.. Diabetes Obes Metab 2008 Nov;10 Suppl 4:63-76.
    doi: 10.1111/j.1463-1326.2008.00945.xpubmed: 18834434google scholar: lookup
  18. Pacini G, Thomaseth K, Ahrén B. Contribution to glucose tolerance of insulin-independent vs. insulin-dependent mechanisms in mice.. Am J Physiol Endocrinol Metab 2001 Oct;281(4):E693-703.
    doi: 10.1152/ajpendo.2001.281.4.E693pubmed: 11551845google scholar: lookup
  19. Lindåse S, Johansson H, Månsby M, Bröjer J. Repeatability of the hyperglycaemic clamp for assessment of β-cell response and insulin sensitivity in horses.. Equine Vet J 2020 Jan;52(1):126-130.
    doi: 10.1111/evj.13119pubmed: 30980682google scholar: lookup
  20. Wallace TM, Levy JC, Matthews DR. Use and abuse of HOMA modeling.. Diabetes Care 2004 Jun;27(6):1487-95.
    doi: 10.2337/diacare.27.6.1487pubmed: 15161807google scholar: lookup
  21. Lindåse S, Nostell K, Bröjer J. A modified oral sugar test for evaluation of insulin and glucose dynamics in horses.. Acta Vet Scand 2016 Oct 20;58(Suppl 1):64.
    doi: 10.1186/s13028-016-0246-zpmc: PMC5073908pubmed: 27766982google scholar: lookup
  22. Bland JM, [Internet]. How should I calculate a within-subject coefficient of variation? [cited 2020 October 11]. Available from: https://www-users.york.ac.uk/~mb55/meas/cv.htm
  23. Pratt SE, Geor RJ, McCutcheon LJ. Repeatability of 2 methods for assessment of insulin sensitivity and glucose dynamics in horses.. J Vet Intern Med 2005 Nov-Dec;19(6):883-8.
    doi: 10.1111/j.1939-1676.2005.tb02782.xpubmed: 16355685google scholar: lookup
  24. Pratt S, Siciliano P, Walston L. Variation of insulin sensitivity estimates in horses. J Equine Vet Sci 2009;29(6):507–12.
    doi: 10.1016/j.jevs.2009.04.194google scholar: lookup
  25. Vaz S, Falkmer T, Passmore AE, Parsons R, Andreou P. The case for using the repeatability coefficient when calculating test-retest reliability.. PLoS One 2013;8(9):e73990.
    doi: 10.1371/journal.pone.0073990pmc: PMC3767825pubmed: 24040139google scholar: lookup
  26. Durham AE, Frank N, McGowan CM, Menzies-Gow NJ, Roelfsema E, Vervuert I, Feige K, Fey K. ECEIM consensus statement on equine metabolic syndrome.. J Vet Intern Med 2019 Mar;33(2):335-349.
    doi: 10.1111/jvim.15423pmc: PMC6430910pubmed: 30724412google scholar: lookup
  27. Warnken T, Huber K, Feige K. Comparison of three different methods for the quantification of equine insulin.. BMC Vet Res 2016 Sep 9;12(1):196.
    doi: 10.1186/s12917-016-0828-zpmc: PMC5016943pubmed: 27613127google scholar: lookup
  28. Rendle D. Laboratory diagnosis of the endocrine causes of laminitis. Livestock 2017;22(4):216–20.
    doi: 10.12968/live.2017.22.4.216google scholar: lookup
  29. Lindåse SS, Nostell KE, Müller CE, Jensen-Waern M, Bröjer JT. Effects of diet-induced weight gain and turnout to pasture on insulin sensitivity in moderately insulin resistant horses.. Am J Vet Res 2016 Mar;77(3):300-9.
    doi: 10.2460/ajvr.77.3.300pubmed: 26919602google scholar: lookup
  30. Nostell K, Lindåse S, Edberg H, Bröjer J. The effect of insulin infusion on heart rate and systemic blood pressure in horses with equine metabolic syndrome.. Equine Vet J 2019 Nov;51(6):733-737.
    doi: 10.1111/evj.13110pubmed: 30887546google scholar: lookup
  31. Öberg J, Bröjer J, Wattle O, Lilliehöök I. Evaluation of an equine-optimized enzyme-linked immunosorbent assay for serum insulin measurement and stability study of equine serum insulin. Comp Clin Path 2012;21(6):1291–300.
    doi: 10.1007/s00580-011-1284-6google scholar: lookup
  32. Giavarina D. Understanding Bland Altman analysis.. Biochem Med (Zagreb) 2015;25(2):141-51.
    doi: 10.11613/BM.2015.015pmc: PMC4470095pubmed: 26110027google scholar: lookup

Citations

This article has been cited 3 times.
  1. Delarocque J, Feige K, Carslake HB, Durham AE, Fey K, Warnken T. Development of a Web App to Convert Blood Insulin Concentrations among Various Immunoassays Used in Horses. Animals (Basel) 2023 Aug 24;13(17).
    doi: 10.3390/ani13172704pubmed: 37684968google scholar: lookup
  2. Williams NJ, Furr M, Navas de Solis C, Campolo A, Davis M, Lacombe VA. Investigating the Relationship Between Cardiac Function and Insulin Sensitivity in Horses: A Pilot Study. Front Vet Sci 2022;9:899951.
    doi: 10.3389/fvets.2022.899951pubmed: 35873691google scholar: lookup
  3. Nolen-Walston RD, Kulp JC, Stefanovski D, van Eps AW. Evaluation of an Automated Fluorescence Enzyme Immunoassay for Quantification of Equine Insulin and Comparison to Five Other Immunoassays. J Vet Intern Med 2025 Mar-Apr;39(2):e70038.
    doi: 10.1111/jvim.70038pubmed: 40048611google scholar: lookup
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